Method of Feature Exaction from Time-series of Spectra to Control Endpoint of Process

ABSTRACT

Methods and systems for using a time-series of spectra to identify endpoint of an etch process. One method includes accessing a virtual carpet that is generated from a time-series of spectra for an etch process. A polynomial with coefficients represents the virtual carpet. The method includes processing a fabrication etch process on a fabrication wafer and generating a carpet defined from a time-series of spectra while processing the fabrication etch process. While the processing the fabrication etch process and generating the carpet, comparing portions of the carpet and the virtual carpet to identify an endpoint metric of the fabrication etch process.

CLAIM OF PRIORITY

This application is a Continuation of U.S. patent application Ser. No.15/389,451, filed on Dec. 23, 2016 (now U.S. Pat. No. 10,262,910, issuedon Apr. 16, 2019) entitled “METHOD OF FEATURE EXACTION FROM TIME-SERIESOF SPECTRA TO CONTROL ENDPOINT OF PROCESS”, which is hereby incorporatedby reference.

FIELD OF THE INVENTION

The present embodiments relate to methods and computer implementedprocesses for examining time-series of spectra information extractedduring processing of etch processing operations in order control etchendpoint operations. The methods and systems utilize training processesto generate three dimensional intensity surface profiles, referred toherein as carpets. Generated carpets during training are then convertedinto a virtual carpet, which is used during real-time processing ofwafers to predict or identify an effective etch depth at a current pointin time, which is then used to determine whether etch endpoints havebeen reached.

BACKGROUND

Plasma has long been employed to process substrates (e.g., wafers orflat panels) to form electronic products (e.g., integrated circuits orflat panel displays). Semiconductor wafers are typically placed in anetch chamber with a mask layer to direct the etch of underlyingmaterials. The etching process removes the underlying materials notcovered by the mask. Although etching processes have been well studiedand precise recipes are often defined for specific structures, materialsand/or material stacks, variations in etch performance still occur. Thereason for this is that etching processes in real-time fabricationenvironments are carried out in different etch chambers. These chambers,although often times tuned and matched, still are not the physically orelectrically the same. In addition, wafers being processed can vary fromwafer to wafer or lot to lot. Still further, variations can beintroduced by the way wafers are placed into each chamber, e.g., waferoff-set variations, wafer tilts, wafer thickness, etc.

As a result, wafer etching processes often utilize at least one type ofend pointing technique. Such techniques can vary from fabricator tofabricator, but most commonly used end pointing may include time basedend-pointing or optical end-pointing. Time based end-pointing relies onuse of pre-calibrated estimates of when a particular etch process shouldend, e.g., so as to remove a predefined amount of material. Opticalend-pointing systems are designed to monitor spectral emissions of theplasma or reflections off the wafer, in an attempt to identify whenchanges in the spectral emissions are indicative of a change of etchmaterial. For example, if the etch has removed all of a certain amountof material, or when a different material starts to be removed, thespectral emissions at the point in time will change. Unfortunately,current techniques still suffer in accuracy, which is challenged evenfurther with the ever shrinking feature sizes. In regard to currentoptical end-pointing, the use of changes in optical conditions isreliant on spectral conditions of one specific point in time.

It is in this context that embodiments arise.

SUMMARY

Methods and systems for examining time-series of spectra informationextracted during processing of etch processing operations in ordercontrol etch endpoint operations. The methods and systems utilizetraining processes to generate three dimensional intensity surfaceprofiles, referred to herein as carpets. A carpet refers to a modelconstructed by multiple sampled frames of intensity spectra information,such that time information of not only a current frame, but of one ormore previous frames, are sampled. As a result, the carpet defines amodel of a series of time (t) samples, and each time sample has itsassociated intensity spectra information (I (k), e.g., wavelength). Thecarpet therefore provides not only spectra information at one specificpoint in time, but also a history of changes in spectra information of agiven wafer throughout its process.

In a training phase, multiple wafers are processed (e.g., etched), andfor each wafer, a carpet is produced. For each carpet, the last framesampled can be associated with an actual measured etch depth typicallyfrom a metrology system. Each carpet is descriptively definedmathematically by polynomials with coefficient values. Polynomialfitting for the training carpets are then processed to define a virtualcarpet with floating coefficients, which is descriptive of all of thecarpets produced during training. Using polynomial coefficients of thevirtual carpet, the virtual frame numbers of the last frame of eachwafer were obtained on virtual carpet. The correlation of these framenumbers against the measured etch depth were further optimized to obtaingood accuracy.

At the end of the training, optimized polynomial coefficients aredownloaded as recipe parameters for run-time execution. During real-timeprocessing of wafers, the polynomial coefficients of the virtual carpetcan be utilized by a controller of the etching system, in order to checkendpoint. The controller, in one embodiment, is generating a carpet fromthe real-time processing. As the frames of the carpet are beinggenerated, a group of consecutive frames (e.g., carpet patch), can befitted to the virtual carpet in order to identify a current virtualframe number. The virtual frame number is pre-correlated to a predictedmetric. In one embodiment, floating parameters, in addition to thevirtual frame number, are used to map to a predicted value for a metric.The predicted value for the metric, when it substantially matches to adesired metric, is indicative of reaching etch endpoint.

Using this process, it is possible to operate end pointing, such thatwhen the predicted etch point is reached, the etch process can end.Additional details regarding the processing for generating trainingcarpets, generating virtual carpets, and real-time use of a virtualcarpet will be described in more detail below with reference to thefigures.

In one embodiment, a method for using a time-series of spectra toidentify endpoint of an etch process is disclosed. The method includesaccessing a virtual carpet that is formed from a time-series of spectrafor the etch process collected during a training operation. And, runninga fabrication etch process on a fabrication wafer, such that while thefabrication etch process is performed portions of a carpet defined froma time-series of spectra is generated for the fabrication etch process.Then, comparing the portions of the carpet of the fabrication etchprocess to the virtual carpet, end pointing is processed for thefabrication etch process when said comparing indicates that a desiredetch depth has been reached for the fabrication wafer. In one example,said portions of the carpet include a current frame of captured spectraand at least one previous frame of captured spectra. The portions of thecarpet of the fabrication etch process are fitted to the virtual carpetto identify a virtual frame number that is correlated to a predicteddepth of etch for the etch process.

In some embodiments, the training operation includes performing the etchprocess on a plurality of wafers, and for each wafer capturing a timeseries of spectra at individually sampled frame times. The captured timeseries of spectra at the individually sampled frame times define arespective carpet, and each respective carpet is characterized by apolynomial with respective coefficients that describe a virtual carpetat different values.

In some embodiments, the coefficients of the respective polynomials ofeach carpet are produced during training are processed by adimensionality reduction algorithm to produce the virtual carpet. Insome embodiments, there are several ways of processing dimensionalityreduction (e.g., stepwise, principle component analysis, etc.). Thevirtual carpet is defined by a standardized polynomial with respectivecoefficients as a superset of all coefficients of training carpets.

In some embodiments, the comparing of the portions of the carpet of thefabrication etch process to the virtual carpet includes fitting theportions of the carpet to the virtual carpet in order to identify avirtual frame number from the virtual carpet. The virtual frame numberis mapped to a predicted etch depth, and said predicted etch depth whenmatched to the desired etch depth is indicative of etch endpoint.

In some embodiments, said training operation includes generating aplurality of carpets from sampled spectra data generated during etchingof substrates, wherein each carpet is produced from substrate beingetched. Then, measuring or receiving data regarding an etch depth foreach of the etched substrates. The process further includes generatingthe virtual carpet from each of the plurality of carpets. The virtualcarpet is defined by a polynomial with coefficients produced by fittinga plurality of polynomials with respective coefficients of each of theplurality of carpets. In one embodiment, some of the polynomialcoefficients of the virtual carpet might be floating and others arefixed or coupled to the floating parameters so that all the polynomialsof the respective coefficients of each of the plurality of carpets are asubset of those of virtual carpet. In one embodiment, said floatingparameters, in addition to the virtual frame number, are used to map toa predicted value for etch depth, and said predicted value for etchdepth when substantially matched to a desired etch depth is indicativeof etch endpoint.

In some embodiments, the time-series of spectra is intensity spectraldata associated with broadband in-situ reflectometry, or is intensityspectral data associated with Optical Emission Spectroscopy (OES), or isellipsometric spectral data associated with broadband in-situellipsometry, wherein said spectral data is collected from a chamberused for etching while a feature is being etch on a wafer.

In another embodiment, a method for generating training data from atime-series of spectra generated during an etching process in a chamberis provided. The method includes etching a plurality of substrates inone or more chambers, wherein while the etching is processed, capturinga plurality of samples of frames of spectra. Each captured frame ofspectra identifies an intensity of the spectra as a function ofwavelength. Then, a metric, e.g., etch depth, is associated with eachsubstrate after said etching is complete, by associating the measuredmetric to the last frame of spectra of the corresponding substrate. Themethod further includes generating a plurality of carpets for each ofthe substrates etched. Each of the carpets is defined by the pluralityof frames of spectra and a polynomial with corresponding coefficientsdefine characteristics of said each of the carpets. The method generatesa virtual carpet by processing the plurality of carpets using apolynomial fitting algorithm, and the virtual carpet is a superset ofthe plurality of carpets, such that every one of the plurality of thecarpets can be projected onto the virtual carpet to determine a virtualframe number. By way of example, due to the flexibility in thedimensionality of virtual carpet, the correlation between projectedvirtual frame number and predicted value of the metric from metrologycan be optimized by one of floating, fixing, and/or coupling the hyperparameters of the virtual carpet. The method includes correlating thevirtual frame numbers of the virtual carpet to a predicted value of themetric. In one embodiment, the method further includes tracking r-squareor adjusted r-square.

In one embodiment, the virtual carpet is accessed by a controller duringreal-time processing of a substrate in order to determine when a currentpredicted value of the metric obtained from the virtual carpetcorresponds to a predefined value that is indicative of endpoint of theetching process in the real-time processing.

In some embodiments, the metric associated with the etching is one of anetch depth, a critical dimension value, wafer bow, or a combination oftwo or more thereof.

In some embodiments, the controller is configured to connect to adatabase or a recipe file, to access the virtual carpet produced duringsaid training for said real-time processing of the substrate.

In some embodiments, during said real-time processing the controller isconfigured to be generating a carpet for the real-time processing and asthe carpet is being generated, at least a portion or a patch of thecarpet is used to compare to said virtual carpet.

In some embodiments, said comparing includes performing a polynomialfitting of said portion or said patch of the carpet against hyperparameters of the virtual carpet, to enable correspondence to thevirtual carpet and said carpet being produced to determine the virtualframe number, such that etch depth can be determined using recipeparameters determined during training. The virtual frame number is thusefficiently determined during said real-time processing.

In some embodiments, said portion of the carpet includes a current frameof captured spectra and at least one previous frame of captured spectra,wherein using said at least one previous frame of captured spectraduring said comparing to said virtual carpet enables accurateidentification of a current state of said real-time processing of saidsubstrate.

In some embodiments, the portion of the carpet is fitted to the virtualcarpet to identify a virtual frame number that is correlated to apredicted depth of etch for the etching process.

Other aspects will become apparent from the following detaileddescription, taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings.

FIG. 1 illustrates a system that includes a chamber used for processinga wafer, in accordance with the one example.

FIG. 2 illustrates an example of a carpet, which is a three-dimensionalrepresentation of the surface generated by the time-series captures offrames, where each frame represents an instance in time that reportsintensity as a function of wavelength.

FIG. 3 illustrates an example cross-section of a feature being etched,to a desired depth, in accordance with one embodiment.

FIG. 4 illustrates more detail regarding the use of a traininggenerator, which includes generating carpets for each of the trainingwafers, in accordance with one embodiment.

FIG. 5 illustrates an example of a carpet, producing its correspondingpolynomial when a wafer (W0) is etched during training, in accordancewith one embodiment.

FIG. 6A illustrates an example of a virtual carpet, having itscorresponding polynomial, which is derived from all of the polynomialsgenerated during the training operation.

FIG. 6B illustrates example polynomial coefficients of training carpets,e.g., carpets produced during training, and in this example an averageis used to define a virtual carpet.

FIG. 7 illustrates an example of a mapping chart between the virtualframe numbers of the virtual carpet and measured depths for the etchoperations performed during the training that produce the variouscarpets, in accordance with one embodiment.

FIG. 8A illustrates an example process of generating training data froma plurality of wafers, to produce a plurality of carpets that are thenfitted to a virtual carpet, in accordance with one embodiment.

FIG. 8B illustrates another example process of generating training datafrom a plurality of wafers, to produce a plurality of carpets that arethen fitted to a virtual carpet, and etch depth's loading in terms ofcarpet polynomial parameters, including virtual carpet numbers and otherfloating polynomial parameters of the carpet, up to 3rd order viaregression, can be determined, in accordance with one embodiment.

FIG. 9 illustrates an example of a system utilized for generating avirtual carpet, and the chamber and controller accessing virtual carpetdata in order to determine etch endpoint, in accordance with oneembodiment.

FIG. 10A illustrates an example process where real-time processing of awafer is being conducted in operation, in accordance with oneembodiment.

FIG. 10B is a flow chart to illustrate the iterative nature ofdimensionality reduction, in accordance with one embodiment.

FIG. 11 is a simplified schematic diagram of a computer system forimplementing embodiments.

DETAILED DESCRIPTION

Methods and systems for examining time-series of spectra informationextracted during processing of etch processing operations in ordercontrol etch endpoint operations. The methods and systems utilizetraining processes to generate three dimensional surface profiles,referred to herein as carpets. A carpet refers to a model constructed bymultiple sampled frames of spectra information, such that timeinformation of not only a current frame, but of one or more previousframes, are sampled. As a result, the carpet defines a model of a seriesof time (t) samples, and each time sample has its associated spectrainformation (λ, e.g., wavelength). The carpet therefore provides notonly spectra information at one specific point in time, but also ahistory of changes in spectra information over one or more prior samplesof spectra information. In one embodiment, an algorithm is definedwherein carpets are generated during training to produce a virtualcarpet. The carpets and virtual carpet are in one embodiment, anextraction of broadband in-situ reflectometry spectra responses viapolynomial regression in both time and spectral dimensions. A carpet isessentially a model of multiple time slices/frames, in which intensityas a function of wavelength is captured for each frame. Thus, for eachtime sample, a frame is captured, which also enables use of one or moreprior frames that were captured, as the carpet is produced. By using thealgorithms/methods described herein, the use of carpet processing duringreal-time processing has an additional benefit of decoupling spectrachanges due to wafer level variations from the time evolution of spectradue to etching.

In one embodiment, machine learning may be implemented to use thetime-series of spectra to extract critical conditions of the wafer. Inone embodiment, a training phase is used, wherein a number of wafers areetch using a target process recipe. The training phase can beimplemented using different process chambers, which implement the targetprocess recipe. Wafer level variations can be introduced in many ways,such as due to variations in previous steps of wafer processing,variations in chambers, variations in wafer properties, variations inwafer lots, variations in possible wafer tilt or rotation, and otherwafer level variations. The result is that etch processes will vary,even when the same target recipe is used on the same machine. However,in accordance with one embodiment, during the processing of each waferduring the training, spectral data are sampled over a period of time ofthe etch process for a plurality of wafers. The sampling thereforeproduces a plurality of sampled frames of spectra information, definedas intensity as a function of k.

The time series of frames therefore define data of a three-dimensional(3D) surface representing intensity, referred to herein as a carpet. Thecarpet, in one embodiment, therefore provides historical information ofchanges in the spectral intensity, not just a single intensity spectragraph. For each wafer used for training, a measurement is made of thedepth of etch. Measurement may be conducted with any number of metrologytools. One example way is to use optical CD (OCD) metrology. OCDmetrology can be used to determine various metrics, including etchdepth, feature characteristics, pre-etch CD, feature or etch profiles,etc. In one embodiment, the measured depth of etch is then correlated tothe last frame of the carpet, which includes spectral intensity at thestate where depth of etch was measured. But, because the carpet alsoholds information regarding previous frames, it is useful to understandwhat the spectral conditions were that lead up to the final frame. Inone embodiment, each carpet produced is mathematically obtained byfitting the experimental spectra with a polynomial or order m*n, havingunique coefficients (C0, . . . Cmn), where m denotes the order in timedimension and n the order in wavelength dimension. Mathematically thefit algorithm is a regression method to minimize the figure of merit,which is defined as the difference of polynomial estimate andexperimental spectra.

In one embodiment, an operation is introduced to reduce dimensionalityof the polynomial coefficients. This dimensionality reduction can beimplemented by either stepwise regression, multi-carpet coupledregression, or principle component analysis. The objective ofdimensionality reduction is to use the least dimensions to account forthe variations among carpets and to correlate successfully with the etchdepth measurement, in terms of floating parameters in these hyperdimensions and virtual frame number representing etch time impact.

In one embodiment, regression was processed by executing a multi-carpetcoupled regression. The algorithm is configured to take as input thepolynomials of each of the carpets generated during the training, andthen fit them into a polynomial with reduced dimension of parameters(C0, . . . Cp), that define a virtual carpet, by using a combined meansquare error (MSE) inclusive of all carpets. By way of example, the MSEis typically defined as:

${MSE} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\; \left( {{\hat{Y}}_{i} - Y_{i}} \right)^{2}}}$

In one embodiment, some polynomial coefficients are coupled across thecarpets, defined by a linear relationship, to represent carpet-to-carpetconstancy while leaving the rest floating. The choice regarding whichparameters to couple and which to float is determined by the impact onthe mean square error between the carpets and experimental spectra.

In another embodiment, dimensionality reduction was processed bystepwise parameter reduction. Correlation of reduced parameter space tothe etch depth measurement, in terms of R square and adjusted R square,is evaluated as parameter space is adjusted to find good correlationwith least parameters. Not all parameters are needed to correlateagainst etch depth measurement.

In still another example implementation, principle component analysiswas used to find the correlation of scores of principle components,virtual frame number, and measured etch depth. Number of principlecomponents can be increased to reach better correlation. Oncesatisfactory correlation is reached to explain measured etch depth withreduced hyper dimensions from above and virtual frame number, trainingis complete.

In the case where the difference of polynomials of training carpets aresmall and virtual carpet frame number itself is enough to account forthe measurement of etch depth with desired accuracy, the polynomials ofvirtual carpet may be obtained by an average of polynomial fitcoefficients.

In another embodiment, the spectral response of a reference wafer may beused to compare the other wafers. Additionally, there are several otherways of linking the polynomial coefficients and virtual frame number, toetch depth. One such method is a partial least square method, and inanother embodiment neural network processing is employed to establish arelationship of parameters to measured etch depth.

Once the training process is complete, the virtual carpet can be usedduring real-time processing of production wafers to determine etchendpoint. In one embodiment, the virtual carpet information is used, inconjunction with real-time spectra, to measure effective etch depth as afunction of spectral history. More information regarding the use of thevirtual carpet will be described with reference to the figures.

In some embodiments, instead of measuring etch depth, the virtual carpetcan be linked to critical dimension (CD) measurements, line width,pitch, spacing, bow detection metrics, and other measurable metrics.That is, for each wafer processed during the training, the resultingcarpet can be correlated to a measured metric, which need not be etchdepth. By way of example, wafer bow is described in Lam ResearchCorporation U.S. Pat. No. 9,123,582, which is incorporated herein byreference.

In one embodiment, during real-time processing (i.e., run-time), thevirtual carpet can be used to predict broadband in-situ reflectometryspectra vs. time and intended target etch depth. This process thereforeenables accurate predication of etch rates at a wafer level, and time tostop the etching. Broadband in-situ reflectometry or interferometermeasures of reflectance of the wafer surface during etching (ordeposition), by focusing a light beam on a spot onto the wafer andmeasuring the intensity of the reflected light in a plurality ofwavelengths. One example of broadband in-situ reflectometry is flashlamp/continuous wave reflectometry (e.g., which is sometimes referred toas Lam Spectral Reflectometer (LSR)). For more related information onin-situ interferometer systems, reference may be made to Lam ResearchCorporation U.S. Pat. Nos. 6,400,458, and 6,160,621, which areincorporated herein by reference.

In another implementation, a dynamic time wrapping (DTW) algorithm canbe used to calculate a matching of spectra against a reference spectra,which can then be directly used to calculate etch rate and ideal etchstop.

There are several advantages of using time series of spectra. Oneadvantage is that it ensures model dependence of causal relations ofspectra. This acts to constrain the modeling parameters and also provideadded accuracy. By way of example, the same spectra of two differenttime series could tell different conditions of the wafer, as bias couldcome from incoming variations. An additional advantage is that thespectral and temporal covariances are explicitly modeled in the virtualcarpet to preserve information content. Thus, there is no loss ofexperimental information. Still further, an advantage of scalability isensured to handle large amount of experimental spectra, as each carpetis fit individually.

Training of the algorithm for active control is faster than many otherphysics based models requiring extensive physical modeling.Additionally, run time execution speeds are also faster than physicallybased models for such complex reflectance from mixed arrays.

It should be understood that the methods described herein are notlimited to intensity spectra. The methods can be applied to any set ofsignals in time appropriately scaled, where within each time frame thecorrelated signal can be represented in ‘x’ with a particular signatureof correlation in the sense of principle components along x as a‘spectra’ in time, and the same dimensionality reduction and trainingstrategies can be adopted. For example, time traces from multiplesensors related to the electrostatic chuck (ESC) can be analyzed insimilar fashion to predict the CD (critical dimension) or CD uniformityin analogy to wavelength time traces from wafer to predict local depth.The covariance of these non-spectral signals can be handled by principlecomponent analysis to extract essential information for given timeframe, therefore enabling endpoint control at higher accuracy.

It will be apparent, that the present embodiments may be practicedwithout some or all of these specific details, for example the etchrate. In other instances, well-known process operations have not beendescribed in detail in order not to unnecessarily obscure the presentembodiments.

FIG. 1 illustrates a system 100 that includes a chamber 102 used forprocessing a wafer 106, in accordance with the one example. In thisexample, plasma 108 is used for processing the wafer 106. The plasma 108can be used for etching features into the wafer 106. Although not shown,the chamber 102 will be connected to a power supply, which is configuredto deliver RF power to an electrode of the chamber 102 in order toproduce the plasma 108. Controller 110 is configured to interface withthe chamber 102, and the RF power supply, in order to control theetching process. In some embodiments, the chamber 102 may be acapacitively coupled plasma (CCP) chamber, or an inductively coupledplasma (ICP) chamber. As further example system, reference may be madeto U.S. Pat. No. 6,979,578, issued to Lam Research Corporation, which isincorporated by reference. In the '578 patent, examples of opticalfibers used in in-situ data collection are shown in FIG. 5.

In either case, the chamber 102 and its processing, is interfaced with acontroller 110, which can provide the settings necessary for processinga recipe for etching by the system 100. An in-situ monitoring device104, may be integrated with the chamber 102 and couple to controller110. In-situ monitoring device 104, may be configured to detect opticalcharacteristics or spectra data of spectral emissions associated withthe processing of the wafer 106. In one embodiment, the in-situmonitoring device 104 is configured to collect and/or sample spectraldata associated with reflectometry or interferometry optical signals, orspectral data associated with Optical Emission Spectroscopy (OES).

In one specific example, the in-situ monitoring device 104 is configuredto generate broadband light that is projected onto the surface of thewafer 106, while a detector collects the spectral data associated withthe reflected light from the surface of the substrate. Although thefollowing discussion primarily focuses on monitoring reflectometry orinterferometry optical signals, the system can be operated using OES, orother inspection techniques.

In one embodiment, the controller 110 is configured to executeprocessing operations that utilize the spectral data collected by thein-situ monitoring device 104, in order to process carpet informationfrom the emissions of the wafer 106. As mentioned above, a carpet isdefined as a collection of frames representing instances of capturedspectral data in a time series. That is, the spectral data is collectedby the in-situ monitoring device 104 at predefined intervals, such as atevery predefined number of milliseconds, seconds, or some custom timesetting.

FIG. 2 illustrates an example of a carpet 120, which is athree-dimensional abstraction of the surface generated by thetime-series captures of frames, where each frame represents an instancein time that categorizes intensity as a function of wavelength. Asshown, frame 0 is the first frame captured for the carpet 120, and eachsubsequent frame up to frame n, represents the carpet for an etchoperation that is illustrated in FIG. 3. Each of the frames 1-n, iscaptured at a specific times, t0-tn. Each frame therefore has its ownrespective spectra that is descriptive of the intensity in terms ofwavelengths. As each frame is captured, the carpet 120 is constructed,therefore exposing information regarding the changes in the intensity interms of wavelength as time progresses.

Thus, information is being gathered not only of a single timeframe whereintensity as a function of wavelength, but also the continual changes ofthe intensity as a function of wavelength for a plurality of times.Thus, at any one point in time, it is possible to ascertain the changesthat occurred that led up to the current state of time. This informationwill expose what intensity changes occur as the substrate material 130is being etched to define etch feature 132. The example shown in FIG. 3shows a single etch feature, but it should be understood that etchingoperations are typically carried out substantially simultaneously forany number of features which could be smaller than the wavelength oflight, and might correspond to a single field or many fields oflithography exposure, distributed throughout a semiconductor wafer. Inthe embodiment where a single reflectometer sensor is used, only thespectra time series under the spot of illumination were collected butused to control the end-point of the entire wafer. When feature criticaldimension and depth changes as etch progresses, diffraction of incomingbeam would generate a change of intensity in the far field as a functionof wavelength, and result in intensity change at the spectrometer.

Thus, the illustration of FIG. 3 is only provided to show that as theetch progresses, frames of spectral intensity as a function ofwavelength will continue to be captured, thus building and defining thecarpet 120. In one embodiment, for a specific wafer processingoperation, such as an etch operation, the feature being etched willreach a specific depth, which is shown in FIG. 3 as a measured depth(dm). At that point, the etch operation is complete, and the carpet 120of FIG. 2 is complete. This results in the last frame (e.g. frame n),being the frame that corresponds to a measured depth dm, at time tn.

The illustration of carpet 120 of FIG. 2, and the etch operation in FIG.3, were shown to illustrate the capture of multiple frames of spectraldata. It should be understood that many more frames will be captured,based on the desired sampling frequency, which can provide a more densecarpet 120 with rich information associated with changes in feature CD,depth, or profile information at the wafer level. In one embodiment, thecarpet 120 is said to change as a function of time, which is uncoveredby the multiple frames captured as a function of wavelength. The carpet120, in one embodiment, can be characterized using a mathematicalpolynomial, with its associated coefficients, for a range ofwavelengths. The coefficients of the polynomial will therefore define asurface in time and wavelength, which can be accessed as will bedescribed below.

As described above, one embodiment described here in utilizes a trainingprocess that requires that multiple wafers be processed for a specificetch recipe and etch process. In some embodiments, the same chamber 102will be used for various wafers. In other embodiments, differentchambers can be used for each of the wafers. Each of the wafersprocessed during the training operation will produce a respective carpet120. Each of the carpets will define the characteristics seen by thein-situ monitoring device 104, in terms of the spectral data captured ateach of the frames, based on the sampling frequency. Once a plurality ofcarpets are defined, these carpets can be fit using a polynomial fitalgorithm to generate a carpet with floated, fixed, and/or coupledcoefficient parameters, which is referred to herein as a virtual carpet.

FIG. 4 illustrates more detail regarding the use of a training generator150, which includes generating carpets for each of the training wafers,in accordance with one embodiment. As shown, the training generator 150includes the generation of carpets 120 a-120 n, where each carpet isassociated with a respective polynomial, and each carpet has a lastframe that is to be correlated to a measured depth of etch. Becausethere will be variations between process conditions, chamberconfigurations, and other factors, it is possible that the etchtermination when generating each of the carpets will be different. Thiseffect would be modeled by the loading of polynomial coefficients viastepwise regression, multi-carpet coupled regression, or principlecomponent analysis, where the parametric difference of different carpetswould reveal its impact on end-point estimate and subsequently bedetermined via linear regression against measured etch depth.

In some embodiments, the various wafers may intentionally be etched todifferent depths, so as to generate various size carpets 120. In eithercase, each of the carpets 120 are captured, in terms of their polynomialand associated coefficients. As mentioned above, the variouscoefficients of the polynomial will be descriptive of thethree-dimensional contour shape of the carpet, which was defined by themultiple frames captured over time for that etch operation. In thisexample, a polynomial fit processor 162 is configured to receive thepolynomials from each of the carpets 120 a-120 n. Additionally, themeasured depths for each of the wafers associated with each of thecarpets 120 a-120 n, will also be captured by a measurement instrument160. The measurement instrument 160 can take on various forms, andbroadly speaking our semiconductor metrology tools that are capable ofmeasuring specific parameters or metrics of a wafer, features, depths,and generally characteristics. Examples include cross-sectional SEM, TEMand scatterometry.

The polynomial fit processor 162 is configured to communicate with avirtual carpet generator 164. The virtual carpet generator 164 is adimensionality reduction and linear regression process by which avirtual carpet 120 is generated. The virtual carpet 120 is configured tohave a predefined size, in terms of frames of spectral data, which isspectral intensity as a function of wavelength. The polynomial fitprocessor 162, as mentioned above, is configured to receive thepolynomials of the various carpets 120 a-120 n, and thus fit them inaccordance with the constraints defined by the virtual carpet generator164. In one embodiment, the virtual carpet generator 164 is configuredto generate a virtual carpet 220, which can be generated by varioustechniques described above.

The virtual carpet 220, is therefore generated, and the virtual carpet220 as well as the measurement instrument 160 outputs are correlated in224 to associate the virtual frame numbers of the virtual carpet to aspecific depth or metric that was measured by measurement instrument160. Thus, during real-time processing and end point operations 226, thecontroller of a chamber can access the virtual carpet 220 and/or thevirtual frame number to depth correlator 224, to identify when and etchprocess has reached end point. End point is reached when the etchingprocess has reached the intended depth for the specific features beingetched, and by use of the virtual carpet, end point can be reached byassociating a portion of a currently processed carpet (i.e., for acurrent fabrication operation), to the virtual carpet 220.

By way of example, real-time processing of real fabrication wafers canutilize this algorithm where the controller is generating a carpet forthe current etch operation. During processing, frames are being producedfor a carpet, which are added to previous frames already produced. Inone embodiment, a current frame and one or more previous frames (i.e., apatch) can be used from the currently generated carpet during real-timeprocessing of an etch, to perform a fitting to the virtual carpet. Byfitting to the virtual carpet in a dynamic and real-time manner, it ispossible to identify a predicted depth of etch in real time. As notedabove, the virtual carpet will hold information regarding virtual framenumbers, which are pre-correlated to etch depths.

As will be described below, the various etch depths can be approximatedfrom the various wafers processed during the training session. And, thatprevious training session produced the virtual carpet, so therefore,information regarding the predicted depth for currently captured framesof spectral data (or a patch of frames), will produce a tightlycorrelated estimate or prediction of the actual etch depth. Thus, bycontinuing to process the carpet during real-time processing, a pointwill arrive where the frames being fitted and mapped to the virtualcarpet will be indicative of the desired depth, for a specific etchoperation. At that point, the controller of the chamber can indicate tothe system that end point has been reached, and the etch operation willbe stopped.

FIG. 5 illustrates an example of a carpet 102 a, producing itscorresponding polynomial when a wafer (W0) is etched during training, inaccordance with one embodiment. In this example, it is shown that carpet120 a was produced as a result to real frame samples 230, which includesframe numbers 231 and time 232. At the completion of the etch operationprocess to generate the carpet 120 a, a final frame from the varioussamples frames is reached. In this example, the final frame is frame467. Frame 467 is only shown as an example number, and the framescaptured will depend on the sampling frequency, and duration of an etchoperation.

Continuing with the example, frame 467 will be associated with ameasured etch depth or some other parameter or metric that is beinginspected or measured by a measurement device or system. As mentionedabove, it is also possible to measure or correlate the frames ofspectral intensity as a function of wavelength for different metrics.Such metrics, may include critical dimension inspections, bowcharacteristics in wafers, and other metrics that are commonly measuredor are measurable.

FIG. 6A illustrates an example of a virtual carpet 220, having itscorresponding polynomial, which is derived from all of the polynomialsgenerated during the training operation. As shown, for this virtualcarpet, virtual frame samples 240 are also identifiable, where virtualframe numbers are associated with different times, which are derivedfrom multiple real frame samples 230, which correspond to all of thepolynomials generated from the various carpets produced from differentwafers during training. The virtual frame samples 240 will also includevirtual frame numbers 241 and the corresponding time 242.

In this example, because the virtual frame numbers have beenstandardized, the virtual frame numbers will extend from virtual framenumber 0 to virtual frame number 300. It is understood that the virtualframe numbers of all of the various training carpets will have differentnumbers of frames, and the various frames and their associatedpolynomial coefficients are derived so that they are standardized to theset of virtual frame numbers defined for the virtual carpet 220. Bygenerating the virtual carpet 220, it is possible to extract out thevariations that occur from the various training carpets, and thusgenerate and eliminate abnormalities or false positives that may haveoccurred in each individual carpet. Further, by generating virtualcarpet 220, is possible to use virtual carpet 220 for later reference byprocesses that are running production wafers, and such production waferscan utilize the virtual carpet 224 identifying end point.

For purposes of providing an example of polynomial coefficientsassociated with wafer runs during training (e.g., wafers 1-5), and aresulting virtual carpet (e.g., average), FIG. 6B below shows someexample numbers. In this example, the average is used to define thevirtual carpet, although other methods may be used. For example, insteadof averaging,

FIG. 7 illustrates an example of a mapping chart 300 between the virtualframe numbers of the virtual carpet 220 and measured depths 304 for theetch operations performed during the training that produce the variouscarpets, in accordance with one embodiment. During processing of afabrication wafer, the controller can be generating its own carpet,defined by a plurality of frames having intensity as a function ofwavelength. As the carpet is being generated, periodically two or moreof the frames, or a patch of the carpet, can be captured and fitted tothe virtual carpet 220. By fitting into the virtual carpet 220, it ispossible to identify the virtual frame number 302 of the most currentframe being processed by the chamber performing the etching on a wafer.

As shown in 310, the current frame number (VFNc) can be identified fromthe virtual frame numbers 302, and correlated to predict the currentdepth dc from the etch depth 304 of the mapping chart 300. As shown inthe mapping chart 300, the various test wafers used during training canalso be mapped to the chart, which will produce a substantially linearapproximation. The linear approximation will show the depths that weremeasured for each of the test wafers, as they were associated to thelast frame in the respective carpets 120. This illustrates that wafer 0was etched to a depth d1, wafer 3 was etched to a depth d2, wafer 1 wasetched to a depth d3, and wafer and was etched to a depth dn.

These steps can be shown to occur substantially along a substantialstraight line, as the virtual frame numbers are a fitted representationof the frames collected from each of the carpets 120. Thus, it isexpected that the standardization provided by the virtual carpet willproduce this substantial linear response or representation.Consequently, during processing, the current virtual frame number VFNc,may be mapped to point 306 along the linear approximation, which canthen be correlated to the predicted current depth dc, during theprocessing. The depth dc is further approximated to lie between depth d2and d3, based on the linear approximation and the identified virtualframe number. The depth dc, in one embodiment, can be identified usinginterpolation. If the real-time processing required that a depth of d3be reached, the system would continue to process the carpet for thecurrent fabrication operations, and will continue to compare two or moreframes or a patch of the currently being generated carpet of a waferwith the virtual carpet, upon fitting the current patch or frames to thevirtual carpet.

Thus, the process can continue to check whether the current virtualframe number corresponds to the desired depth d3. Once the systemprocessing the production wafer reaches to VFN5, for example, a depth d3will be reached, and the controller will instruct the etch process tostop.

FIG. 8A illustrates an example process of generating training data froma plurality of wafers, to produce a plurality of carpets that are thenfitted to a virtual carpet, in accordance with one embodiment. Inoperation 402, training data it is generated from a plurality of etchprocesses of a plurality of wafers. As mentioned above, the same etchsystem or various etch systems configured similarly, can process anumber of wafers, and during the processing, intensity as a function ofwavelength can be captured. In operation 404, a carpet for each of theprocesses conducted for each of the wafers is produced.

The carpet will contain a plurality of sampled frames of intensity as afunction of wavelength. When the process etching is complete for thetraining wafer, operation 406 will measure a resulting depth for eachwafer, such that a last frame in each carpet will corresponds to theresulting depth that was measured. On one example, a metrology systemmay be used to conduct the measurements. In operation 408, a polynomialfit is processed for each of the produce carpets to produce a virtualcarpet. Some of the polynomial coefficients of the virtual carpet mightbe floating and others are fixed or coupled to the floating parametersso that all the polynomials of the respective coefficients of each ofthe plurality of carpets are a subset of those of virtual carpet. Thevirtual carpet is therefore a super set of the plurality of carpetsproduced during processing of wafers during training. In operation 410,a correlation is generated between virtual frame numbers of the virtualcarpet to predicted depths of etch. This includes conducting supervisedtraining of virtual frame numbers of the virtual carpet to predictdepths of etch or a metric.

By way of example, the correlation is shown in FIG. 7, by way of themapping chart 300. In operation 412, the virtual carpet and thecorrelation is stored to a database for use during real-time processingof wafers.

FIG. 8B is another example of the process of FIG. 8A, with additionaldetail provide in regard to operations 410 and 412. In this example,operation 410′ describes that etch depth's loading can be defined interms of carpet polynomial parameters. Such parameters may includevirtual carpet frame numbers and other floating polynomial parameters ofthe carpet, and in some cases up to 3rd order via regression. Inoperation 412′, the polynomials of virtual carpet are stored. Thepolynomials can be stored in a database as either as floating, fixed,and/or coupled parameters and associated constants. In this example, thecoefficients of the regression are obtained in 410′.

As used herein, real-time processing a wafers means that productionwafers are being processed, and the endpoint mechanisms utilizedimplement the use of fitting produced carpet patches to a virtualcarpet, that was generated during a prior training operation. In someembodiments, the controller of the chamber can process the correlationof the carpet being generated to the virtual carpet. In otherimplementations, a separate computer or even a network computer canaccess the virtual carpet and produce the results from the comparison,the fitting operations, and the resulting endpoint determinations.

In further embodiments, the process can be shared by one or morecomputers or one or more processes, in the form of real computers orvirtualized computers. In some embodiments, the processing can bedistributed among a plurality of virtual machines. In either manner, theprocessing of fabrication wafers can implement a virtual carpet, suchthat carpets being produced during fabrication can be compared to thevirtual carpet in order to determine endpoint or verify a metricassociated with the etching process. As mentioned above, measurementscan be made of etch depths. However measurements can be made of anynumber of feature metrics, such as wafer characteristics, criticaldimensions, wafer bow, and the like.

FIG. 9 illustrates an example of a system utilized for generating avirtual carpet 220, and the chamber and controller accessing virtualcarpet data in order to determine etch endpoint or some other metric, inaccordance with one embodiment. As shown, process characterizationengine 500, is configured to define functional operations performed byvarious systems. Training generator 150, can include process operationsand instructions that can communicate with one or more chambers, forrunning the training operations on one or more wafers.

The results of the training will produce respective carpets 120, whichare then used to produce a virtual carpet 220. The measurementinstrument 502 can be utilized to measure the resulting etch depths,feature parameters, or other metrics, which are associated to the lastframe in the carpet 120 produced by each of the training system orsystems. A virtual frame number to depth correlator 506 may be provided,where etch depth is the metric being measured. The correlator 506, inone embodiment, may be defined as code or instructions or data that canbe stored in a database 504, which includes data of the virtual carpet220. In another embodiment, the virtual frame number to depth or metriccorrelator 506 can be stored in a recipe file, hardcoded data, or suchdata can be retrieved from a server. Recipe transfer can be implemented,for example, via 734 and/or 714 as shown in FIG. 11. In eitherconfiguration, the virtual frame number to depth or metric correlator506 is made accessible to real-time processing of wafers. Duringreal-time processing, the controller 110 will obtain or download datafrom the database 504, i.e., virtual carpet data, for use.

As shown, chamber 102 will have its corresponding in-situ monitoringdevice 104. The controller 110 will be interfaced with the chamber 102and the in-situ monitoring device 104. The controller 110 will beconfigured to generate a carpet in real-time, by real-time carpetgenerator 520. The real-time carpet generator 520 is essentiallyproducing frames that represent intensity as a function of wavelengthduring each sample. Thus, even before an etch process is complete, thereal-time carpet generate 520 will be busy generating more and moreframes for each of the corresponding predefined sample times. Inoperation 522, a polynomial fit to the virtual carpet is performed.

To do this, the polynomial and associated coefficients of the polynomialfor at least a portion of the carpet being generated by generator 520,will be fit to the virtual carpet 220 in order to identify a virtualframe number from the virtual frame number to depth or metric correlator506. The result is that the controller 110 will receive or identify acurrent etch depth or metric 524. As shown in FIG. 7, this process mayentail the identification of a virtual frame number that has beencorrelated to an etch depth or metric, from the look up to the virtualcarpet by the controller 110. Once the etch depth has been reached, byvirtue of the continuous or iterative lookups to the virtual carpet bythe controller 110, as the real-time carpet generator 520 continues toprocess, the etch endpoint processor 526, may identify that the etchdepth that is predicted by use of the virtual carpet 220, corresponds tothe desired depth etch. At that point, the system will determine thatendpoint has been reached.

FIG. 10A illustrates an example process where real-time processing of awafer is being conducted in operation 602, in accordance with oneembodiment. As shown, the real-time wafer processing may be performed bya fabrication chamber, such as chamber 102, that is coupled to orconnected to an in-situ monitoring device 104. In some embodiments, thechamber 102 may be installed in a fabrication facility, along with manyother chambers. Each of the chambers can themselves be connected to thein-situ monitoring device 104, such that spectral data can be collectedfor a plurality of frames over a time series.

In operation 604, a partial carpet is generated from the plurality offrames captured during processing of a current etch operation. Asmentioned above, during fabrication processing, a carpet is continuouslybeing produced, by adding more and more frames at predefined samplingrates, to define the current carpet. At periodic points in time, whichcan be programmatically set, the controller of the system or a separateprocess, can trigger that a polynomial fit of the partial carpet be madeto the virtual carpet (i.e., the virtual carpet having been previouslygenerated during training) to characterize the process associated withthe current etch operation, as per operation 606.

In operation 608, a virtual frame number and other carpet polynomialcoefficients are identified from data associated with the virtualcarpet.

In operation 610, a predicted depth of etch is identified based on theidentified virtual frame number, as shown with reference to the exampleof FIG. 7. In one embodiment, the prediction of the etch depth will usethe virtual frame number as well as other carpet polynomialcoefficients. By way of example, at least part of the prediction comesfrom the virtual frame number, but the polynomial coefficients floatedin the run-time process captures the differences of the partial carpetsand provides a correction (via the pre-determined loading parameters) tothe prediction. In operation 612, it is determined whether endpoint hasbeen reached. If endpoint has not been reached, the system will continueto process another portion of the partial carpet, which includes thelast or most currently processed frame, and will proceed throughoperation 606, 608 and 610, until complete. Once the process end pointhas been reached, meaning that the desired etch depth has been reachedand corresponds to the predicted depth etch in operation 610, the etchoperation will be stopped.

FIG. 10B is a flow chart to illustrate the iterative nature ofdimensionality reduction, in accordance with one embodiment. Inoperation (a), a virtual carpet is defined using polynomial coefficientsobtained by ensemble average, where all coefficients are fixed. Asmentioned above, having fixed coefficients is in accordance with oneimplementation. In operation (b), a virtual frame number for each carpetgenerated during training is obtained. In one embodiment, one virtualframe number is obtained for each training carpet to generate an X/Yscatter plot with measured etch depth, to determine slope and intercept.This provides for the correlation, as discussed in this application.

In operation (c), the virtual frame number is correlated only tomeasured etch depth. In this example, up to operation (c), when all ofthe virtual carpet polynomial coefficients are fixed, there is onlyvirtual frame number that would vary out of virtual carpet fitting.Thus, this only this information is used to correlate with measured etchdepth and check against prediction accuracy. If it is not good, as in(d) below, the process will need to start to introduce floatingparameters to the virtual carpet which would be determined along withvirtual frame number, again one set of parameters per training carpet.We would then use VFN+1, VFN+2, . . . VFN+q parameters to predictmeasured etch depth. This process is referred to as feature extraction.

Thus, in operation (d), a check is made against a predefined accuracythat is needed. If result of step (c) is good enough, the process stops.This means that the virtual carpet is accurately predicting etch depth.If the accuracy needed is not being reached, in operation (e) thefloating parameter space of the virtual carpet and lower the mean squareerror is expanded. In operation (f), stepwise regression, multi-carpetcoupled regression, or PCA is used to reduce the dimensions obtained in(e).

In operation (g), based on hyper dimensions defined at step (f), theprocess proceeds to obtain the virtual frame number for each carpetgenerated during training while floating the loading parameters of thosehyper dimensions. As can be appreciated, multiple steps of virtualcarpet evaluation occur during the training process. Thus, we areiteratively improving the quality of virtual carpet correlation againstmeasured depth. Within each iteration of (e)-(i), we redefine virtualcarpet dimensions.

Further, certain carpet-specific loading parameters are obtained forthose floating dimensions at the end of regression, along with virtualframe number. In operation (h), correlation is performed of the loadingparameters and virtual carpet number from step (g) to measured etchdepth via linear regression. In operation (i) a check against accuracyis needed. If result is good enough, stop. As used herein, “good enough”means the difference of a supervised training and reference metrology issmall enough, such that in situ process control using a virtual carpetis considered a valid replacement of a standalone ex-situ metrologysystem.

If the results in (i) are not good enough, then in operation (j),further reduce hyper dimensions and iterate from operation (g).

In operation (k), if there are no more hyper dimensions to reduce,expand the floating parameter space again and iterate operation (e). Byintroducing higher order polynomials, mean square error will keep comingdown. In operation (1), in one embodiment, mean square error (MSE), canbe substituted by an unbiased estimate of error variance, e.g., theresidual sum of squares divided by the number of degrees of freedom.

Although specific examples were provided regarding the generation ofcarpets using measured broadband in-situ reflectometry spectra, stillother methods of measuring can be used. Further, laser methods likelaser absorption spectrometry may be used. In one example, laserabsorption with a carpet on integration band or laser absorptionspectroscopy with full spectra, may be used. In still other embodiments,RF signals which also have frequency spectra that are known to displaysimilar complicated carpet behaviors related to both on-wafer metricchanges, chamber parts, plasma impedance (chemistry) changes, may alsobe amenable to the analyses disclosed. In regard to RF signals, it isbelieved that metrics obtained will be less about endpoint and moreabout or useful for chamber matching/metrification.

In some embodiments, the spectral data that is collects is associatedwith light or laser interferometry, or reflectometry and absorption, orOES, or RF voltage and current traces themselves or mathematicallytransformed into RF spectral amplitude. In one embodiment, the spectraldata is collected from a chamber used for etching while a feature isbeing etched on a wafer.

In still other embodiments, more data streams can be put together tomake synthetic ‘spectra’ that have carpet like behaviors. One usefulnessof using a carpet, as described herein, is the physically constrainedstrong correlation and continuity relationships between any spectralelement and its near-spectral-dimension neighbor and itsnear-temporal-dimension neighbors. If different tool data is used inconjunction with the spectra collected, the law-of-nature-enforcedcontinuity of correlation in ‘spectral’ and ‘temporal’ space may bereduced. This is because the tool-data variables are not necessarily‘near’ each other due to physics. In one embodiment, it is possible tosort the tool data to either find the physics to put tool-data variables‘next to’ each other or we would need to mathematically select and orderthe variables so ‘by discovery’ for a ‘good operating tool’ thevariables so arranged, in a ‘pseudo-spectra’ known to have‘spectro-temporal’ correlation and continuity.

In this manner, it is possible to use carpet processing to call controlactions and detect differences between tools. In one embodiment, thecontroller 110, described with reference to FIGS. 1 and 9 above mayinclude a processor, memory, software logic, hardware logic and inputand output subsystems from communicating with, monitoring andcontrolling a plasma processing system. The controller 110 may alsohandle processing of one or more recipes including multiple set pointsfor various operating parameters (e.g., voltage, current, frequency,pressure, flow rate, power, temperature, etc.), e.g., for operating aplasma processing system. Furthermore, although more detailed exampleswere provide with reference to etching operations (e.g., etching tools),it should be understood that the operations can equally be utilized fordeposition operations (e.g., deposition tools). For example, in theverification operations, instead of verifying etch performance, theverification can be of deposition performance. Deposition performancecan be quantified in various ways, and without limitation, various typesof metrology methods and/or tools may be used. Furthermore, depositionperformance may be measured, sensed, approximated, and/or tested in-situor off-line.

In some implementations, a controller 110 is part of a system, which maybe part of the above-described examples. Such systems can comprisesemiconductor processing equipment, including a processing tool ortools, chamber or chambers, a platform or platforms for processing,and/or specific processing components (a wafer pedestal, a gas flowsystem, etc.). These systems may be integrated with electronics forcontrolling their operation before, during, and after processing of asemiconductor wafer or substrate. The electronics may be referred to asthe “controller,” which may control various components or subparts ofthe system or systems. The controller 110, depending on the processingrequirements and/or the type of system, may be programmed to control anyof the processes disclosed herein, including the delivery of processinggases, temperature settings (e.g., heating and/or cooling), pressuresettings, vacuum settings, power settings, radio frequency (RF)generator settings, RF matching circuit settings, frequency settings,flow rate settings, fluid delivery settings, positional and operationsettings, wafer transfers into and out of a tool and other transfertools and/or load locks connected to or interfaced with a specificsystem.

Broadly speaking, the controller 110 may be defined as electronicshaving various integrated circuits, logic, memory, and/or software thatreceive instructions, issue instructions, control operation, enablecleaning operations, enable endpoint measurements, and the like. Theintegrated circuits may include chips in the form of firmware that storeprogram instructions, digital signal processors (DSPs), chips defined asapplication specific integrated circuits (ASICs), and/or one or moremicroprocessors, or microcontrollers that execute program instructions(e.g., software). Program instructions may be instructions communicatedto the controller 110 in the form of various individual settings (orprogram files), defining operational parameters for carrying out aparticular process on or for a semiconductor wafer or to a system. Theoperational parameters may, in some embodiments, be part of a recipedefined by a process that is engineered to accomplish one or moreprocessing steps during the fabrication of one or more layers,materials, metals, oxides, silicon, silicon dioxide, surfaces, circuits,and/or dies of a wafer.

The controller 110, in some implementations, may be a part of or coupledto a computer that is integrated with, coupled to the system, otherwisenetworked to the system, or a combination thereof. For example, thecontroller 110 may be in the “cloud” or all or a part of a fab hostcomputer system, which can allow for remote access of the waferprocessing. The computer may enable remote access to the system tomonitor current progress of fabrication operations, examine a history ofpast fabrication operations, examine trends or performance metrics froma plurality of fabrication operations, to change parameters of currentprocessing, to set processing steps to follow a current processing, orto start a new process. In some examples, a remote computer (e.g. aserver) can provide process recipes to a system over a network, whichmay include a local network or the Internet. The remote computer mayinclude a user interface that enables entry or programming of parametersand/or settings, which are then communicated to the system from theremote computer.

In some examples, the controller 110 receives instructions in the formof data, which specify parameters for each of the processing steps to beperformed during one or more operations. It should be understood thatthe parameters may be specific to the type of process to be performedand the type of tool that the controller 110 is configured to interfacewith or control. Thus as described above, the controller 110 may bedistributed, such as by comprising one or more discrete controller 110 sthat are networked together and working towards a common purpose, suchas the processes and controls described herein. An example of adistributed controller 110 for such purposes would be one or moreintegrated circuits on a chamber in communication with one or moreintegrated circuits located remotely (such as at the platform level oras part of a remote computer) that combine to control a process on thechamber.

Without limitation, example systems may include a plasma etch chamber ormodule, a deposition chamber or module, a spin-rinse chamber or module,a metal plating chamber or module, a clean chamber or module, a beveledge etch chamber or module, a physical vapor deposition (PVD) chamberor module, a chemical vapor deposition (CVD) chamber or module, anatomic layer deposition (ALD) chamber or module, an atomic layer etch(ALE) chamber or module, an ion implantation chamber or module, a trackchamber or module, and any other semiconductor processing systems thatmay be associated or used in the fabrication and/or manufacturing ofsemiconductor wafers.

As noted above, depending on the process step or steps to be performedby the tool, the controller 110 might communicate with one or more ofother tool circuits or modules, other tool components, cluster tools,other tool interfaces, adjacent tools, neighboring tools, tools locatedthroughout a factory, a main computer, another controller 110, or toolsused in material transport that bring containers of wafers to and fromtool locations and/or load ports in a semiconductor manufacturingfactory.

FIG. 11 is a simplified schematic diagram of a computer system forimplementing embodiments. It should be appreciated that the methodsdescribed herein may be performed with a digital processing system, suchas a conventional, general-purpose computer system. Special purposecomputers, which are designed or programmed to perform only one functionmay be used in the alternative. The computer system includes a centralprocessing unit (CPU) 704, which is coupled through bus 710 to randomaccess memory (RAM) 706, read-only memory (ROM) 712, and mass storagedevice 714. System controller program 708 resides in random accessmemory (RAM) 706, but can also reside in mass storage 714.

Mass storage device 714 represents a persistent data storage device suchas a floppy disc drive or a fixed disc drive, which may be local orremote. Network interface 730 provides connections via network 732,allowing communications with other devices. It should be appreciatedthat CPU 704 may be embodied in a general-purpose processor, a specialpurpose processor, or a specially programmed logic device. Input/Output(I/O) interface provides communication with different peripherals and isconnected with CPU 704, RAM 706, ROM 712, and mass storage device 714,through bus 710. Sample peripherals include display 718, keyboard 722,cursor control 724, removable media device 734, etc.

Display 718 is configured to display the user interfaces describedherein. Keyboard 722, cursor control 724, removable media device 734,and other peripherals are coupled to I/O interface 720 in order tocommunicate information in command selections to CPU 704. It should beappreciated that data to and from external devices may be communicatedthrough I/O interface 720. The embodiments can also be practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a wire-based or wirelessnetwork.

Embodiments may be practiced with various computer system configurationsincluding hand-held devices, microprocessor systems,microprocessor-based or programmable consumer electronics,minicomputers, mainframe computers and the like. The embodiments canalso be practiced in distributed computing environments where tasks areperformed by remote processing devices that are linked through anetwork.

With the above embodiments in mind, it should be understood that theembodiments can employ various computer-implemented operations involvingdata stored in computer systems. These operations are those requiringphysical manipulation of physical quantities. Any of the operationsdescribed herein that form part of the embodiments are useful machineoperations. The embodiments also relate to a device or an apparatus forperforming these operations. The apparatus may be specially constructedfor the required purpose, such as a special purpose computer. Whendefined as a special purpose computer, the computer can also performother processing, program execution or routines that are not part of thespecial purpose, while still being capable of operating for the specialpurpose. Alternatively, the operations may be processed by a generalpurpose computer selectively activated or configured by one or morecomputer programs stored in the computer memory, cache, or obtained overa network. When data is obtained over a network the data may beprocessed by other computers on the network, e.g., a cloud of computingresources.

One or more embodiments can also be fabricated as computer readable codeon a computer readable medium. The computer readable medium is any datastorage device that can store data, which can thereafter be read by acomputer system. Examples of the computer readable medium include harddrives, network attached storage (NAS), read-only memory, random-accessmemory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes and other optical andnon-optical data storage devices. The computer readable medium caninclude computer readable tangible medium distributed over anetwork-coupled computer system so that the computer readable code isstored and executed in a distributed fashion.

Although the method operations were described in a specific order, itshould be understood that other housekeeping operations may be performedin between operations, or operations may be adjusted so that they occurat slightly different times, or may be distributed in a system whichallows the occurrence of the processing operations at various intervalsassociated with the processing, as long as the processing of the overlayoperations are performed in the desired way.

For more information on methods for monitoring process conditions andmethods for adjusting settings, reference may be made to U.S.Provisional Patent Application No. 62/370,658, filed on Aug. 3, 2016,entitled “Methods and Systems for Monitoring Plasma Processing Systemsand Advanced Process and Tool Control,” U.S. Pat. No. 6,622,286,entitled “Integrated electronic hardware for wafer processing controland diagnostic,” U.S. Pat. No. 8,295,966, entitled “Methods andapparatus to predict etch rate uniformity for qualification of a plasmachamber,” U.S. Pat. No. 8,983,631, entitled “Arrangement for identifyinguncontrolled events at the process module level and methods thereof,”U.S. Pat. No. 8,473,089, entitled “Methods and apparatus for predictivepreventive maintenance of processing chambers,” U.S. Pat. No. 8,271,121,entitled “Methods and arrangements for in-situ process monitoring andcontrol for plasma processing tools,” and U.S. Pat. No. 8,538,572,entitled “Methods for constructing an optimal endpoint algorithm,” allof which are assigned to Lam Research Corporation, the assignee of thepresent application and each of which are incorporated herein for allpurposes.

For additional information regarding machine learning algorithms,phenomenological models and associated processes, reference may be madeto a Theses entitled “Virtual Metrology for Semiconductor ManufacturingApplications,” by Bertorelle Nicola, University of Padua, Department ofInformation Engineering, dated 28 Jun. 2010; a Theses entitled“Statistical Methods for Semiconductor Manufacturing,” by Gian AntonioSusto, Universita Degli Studi di Padova, School in InformationEngineering, January 2013; and a paper entitled “Etching characteristicsand mechanisms of the MgO thin films in the CF4/Ar inductively coupledplasma,” by A. Efremov, et al. Department of Electronic Devices andMaterials Technology, Sate University of Chemistry and Technology, 7, F.Engels St., 15300 Ivanovo, Russia, Jan. 12, 2007, each of which isherein incorporated by reference.

Further, embodiments and any specific features described in the aboveincorporated by reference documents and applications may be combinedwith one or more features described herein, to define or enable specificembodiments.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, it will be apparent thatcertain changes and modifications can be practiced within the scope ofthe appended claims. Accordingly, the present embodiments are to beconsidered as illustrative and not restrictive, and the embodiments arenot to be limited to the details given herein, but may be modifiedwithin the scope and equivalents of the appended claims.

1. A method, comprising, accessing a virtual carpet that is generatedfrom a time-series of spectra for an etch process collected during atraining, the training producing a polynomial with coefficients thatrepresent the virtual carpet; running a fabrication etch process on afabrication wafer, such that while the fabrication etch process isperformed portions of a carpet defined from a time-series of spectra isgenerated for the fabrication etch process; comparing the portions ofthe carpet of the fabrication etch process to the virtual carpet; andtriggering an endpoint of the fabrication etch process when saidcomparing indicates that a desired etch metric has been reached.
 2. Themethod of claim 1, wherein said portions of the carpet and the virtualcarpet include frames associated with specific times.
 3. The method ofclaim 1, wherein the etch metric is associated with at least one of apredefined etch depth, a predefined critical dimension value, apredefined wafer bow value, a predetermined line width value, apredefined feature pitch value, a predefined features spacing value, orpredetermined measurable value or a combination of two or more thereof.4. The method of claim 1, wherein each portion of said carpet includesat least two frames and each portion is a patch of the carpet.
 5. Themethod of claim 1, wherein the portions of the carpet of the fabricationetch process are fitted to the virtual carpet to identify a virtualframe number in the virtual carpet that is correlated to a predictedvalue for said etch metric.
 6. The method of claim 1, wherein thecoefficients of the polynomial are processed by a dimensionalityreduction algorithm to produce the virtual carpet.
 7. The method ofclaim 6, wherein the polynomial of the virtual carpet is a standardizedpolynomial with respective coefficients that are a superset ofcoefficients obtained from carpets produced during said training.
 8. Themethod of claim 6, wherein comparing the portions of the carpet of thefabrication etch process to the virtual carpet includes fitting theportions of the carpet to the virtual carpet in order to identify avirtual frame number from the virtual carpet.
 9. The method of claim 8,wherein the virtual frame number is mapped to a predicted value for theetch metric.
 10. The method of claim 1, wherein at least some of thecoefficients of the virtual carpet are floating parameters and othersare fixed or coupled to the floating parameters.
 11. The method of claim10, wherein said floating parameters in addition to a virtual framenumber are used to map to a predicted value for the etch metric.
 12. Themethod of claim 1, wherein the time-series of spectra is intensityspectral data associated with broadband in-situ reflectometry, or isintensity spectral data associated with Optical Emission Spectroscopy(OES), or is ellipsometric spectral data associated with broadbandin-situ ellipsometry, wherein said spectral data is collected from achamber used for etching while a feature is being etched on a wafer. 13.A method, comprising, accessing a virtual carpet that is generated froma time-series of spectra for an etch process, a polynomial withcoefficients representing the virtual carpet; processing a fabricationetch process on a fabrication wafer; and generating a carpet definedfrom a time-series of spectra while processing the fabrication etchprocess, and while the processing the fabrication etch process andgenerating the carpet, comparing portions of the carpet and the virtualcarpet to identify an endpoint metric of the fabrication etch process.14. The method of claim 13, wherein said portions of the carpet and thevirtual carpet include frames associated with specific times.
 15. Themethod of claim 13, wherein the endpoint metric is associated with atleast one of a predefined etch depth, a predefined critical dimensionvalue, a predefined wafer bow value, a predetermined line width value, apredefined feature pitch value, a predefined features spacing value, orpredetermined measurable value or a combination of two or more thereof.16. The method of claim 13, wherein the portions of the carpet of thefabrication etch process are fitted to the virtual carpet to identify avirtual frame number in the virtual carpet that is correlated to apredicted value for said endpoint metric.
 17. The method of claim 13,wherein the coefficients of the polynomial are processed by adimensionality reduction algorithm to produce the virtual carpet. 18.The method of claim 17, wherein the polynomial of the virtual carpet isa standardized polynomial with respective coefficients that are asuperset of coefficients obtained from carpets produced during training.19. The method of claim 13, wherein comparing the portions of the carpetof the fabrication etch process to the virtual carpet includes fittingthe portions of the carpet to the virtual carpet in order to identify avirtual frame number from the virtual carpet.
 20. The method of claim13, wherein the time-series of spectra is intensity spectral dataassociated with broadband in-situ reflectometry, or is intensityspectral data associated with Optical Emission Spectroscopy (OES), or isellipsometric spectral data associated with broadband in-situellipsometry, wherein said spectral data is collected from a chamberused for etching while a feature is being etched on a wafer.